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Optimal learning rate scaling in deep networks depends on data, research finds

By PulseAugur Editorial · [1 sources] ·

A new research note explores the dynamics of deep scalar linear networks, demonstrating that optimal learning rate scaling is data-dependent. The study shows that data-agnostic scaling rules falter across different network depths. However, when optimal data-dependent scaling is applied, learning dynamics become independent of the data and only weakly dependent on depth, leading to consistent linear convergence rates across all depths, including infinite depth. This data-dependent effect was also observed in networks incorporating residual connections. AI

IMPACT Provides theoretical insights into optimizing training dynamics for deep learning models.

RANK_REASON Research paper published on arXiv detailing findings about deep learning networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Optimal learning rate scaling in deep networks depends on data, research finds

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Research paper published on arXiv detailing findings about deep learning networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yedi Zhang, Peter E. Latham, Leena Chennuru Vankadara, Andrew Saxe ·

    Optimal Learning Rate Scaling Depends on Data in Deep Scalar Linear Networks

    arXiv:2607.07884v1 Announce Type: new Abstract: In this short note we consider the gradient descent dynamics of deep scalar linear networks, $f(x) = \prod_{l=1}^L w_l x$, which enjoy exact time-course solutions for any integer depth. We show that even in this minimal model, the o…

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